AI data labeling is the process of tagging raw data (such as text, images, or audio) with accurate labels. It allows AI models to recognise patterns and make accurate predictions. Some popular labeling types are NLP, image processing, data tagging, and video annotation.
Garbage In, Garbage Out! Do you know how leading AI models like ChatGPT, DeepSeek and Gemini generate near-perfect responses?
If we were to answer in three words, we’d say it all depends on the “high-quality labeled datasets”. Studies show that generative AI systems like ChatGPT heavily rely on large-scale labeled datasets that have over 1,60,000 dialogues. It helps them deliver reliable AI predictions.
In contrast, poor data quality leads to suboptimal model performance. But, how does this data quality improve? Is there a technique? Yes, it is known as AI data labeling.
In it, developers give names or tags to different pieces of data. The better the labels, the better the AI model works! Want to know more? In this article, let’s understand what AI data labeling is and its various types. Also, we will see some popular approaches to AI data labeling.
What is AI Data Labeling?
AI data labeling is the technique of giving a name or tag to pieces of data like:
- Text
- Images
- Videos
Such tagging allows computers to understand what that data is about. It is an important step in the pre-processing stage and is usually performed before using any data to train an AI model.
For example,
- Say you show a computer 1,000 pictures of cats and dogs.
- Now, you must tell the computer which image shows a cat and which one shows a dog.
- That “cat” or “dog” tag is called a label.
- Once the computer sees enough labeled examples, it can start recognising new pictures on its own.
AI data labeling is primarily used in these three core areas:
- Computer Vision (say identifying objects in photos)
- Natural Language Processing (say tagging emotions or topics in customer reviews)
- Speech Recognition (say marking different words in an audio clip)
Please note that without labeled data, AI systems would not know what they are looking at or working with.
5 Latest Types of AI Data Labeling in 2025
The global AI market is expected to become 5X in the coming years! That’s largely because both large and small businesses have started using AI in their daily operations. Studies show that about 87 out of every 100 companies believe using AI can help them do better than their competitors.
However, the quality of responses generated by an AI model largely depends on AI data labeling. For a stronger hold on the concept, check out the five latest types of tagging performed by leading AI data labeling companies:
1. Image Processing
Image processing means labeling different parts of an image. This lets a machine understand what is shown. This type of AI data labeling covers tagging:
- Objects
- People
- Animals
- Scenes
- Text
Here, human labelers mark the images correctly so that AI models learn from accurate data. For example,
- In facial recognition, labels are added to identify features like eyes, nose, and mouth.
- In object detection, the AI learns to recognise items like cars, chairs, or traffic signs.
Nowadays, image processing is widely used in these industries:
- Healthcare (to detect diseases in scans)
- Agriculture (to monitor crops)
- Security (to detect suspicious objects)
2. Video Annotation
Video annotation is similar to image processing but done on moving visuals. In this type of AI data labeling, objects are tagged across multiple frames of a video. Since objects move in videos, the task is more complex!
Thus, most AI data labeling companies use human annotators to label the same object throughout the video. This allows the AI to understand how it moves and changes.
Some popular techniques for accurate tagging are:
- Bounding boxes (drawing boxes around objects)
- Polygon annotations (drawing the exact shape)
- Event tracking (labeling actions like walking or falling)
This AI data labeling technique is primarily used in self-driving cars, where the system needs to identify and track:
- Other vehicles
- Pedestrians
- Traffic signs
3. Data Tagging
Data tagging means labeling data with relevant keywords. This makes it easier to:
- Sort
- Search
- Analyse
This AI data labeling technique is commonly used in E-commerce platforms. For example,
- A product image of a blue jacket might be tagged with words like:
- “Blue”
- “Jacket”
- “Winterwear”
- “Zippered”
- These tags allow users to find the product through a search or recommendation engine.
This type also finds its application in machine learning. Here, tagging allows an AI model to learn how to categorise and retrieve information.
4. Data Digitisation
In data digitisation, non-digital (analogue) information is converted into digital formats. This type of AI data labeling scans:
- Printed documents
- Handwritten notes
- Photos
- Old records
Post-scanning it is turned into readable files like PDFs or Word documents. The data itself remains unchanged. However, once it’s digital, AI tools can now analyse, label, and store it for processing.
Usually, human labelers step in after digitisation. Most AI data labeling companies use their services to tag important fields, such as:
- Names
- Dates
- Amounts
This type of tagging is crucial in industries like finance, insurance, and law where documents must be searchable and usable for analysis or audits.
5. Natural Language Processing (NLP)
Natural Language Processing (NLP) is a field of AI where machines are trained to understand and work with human language (both written and spoken). AI data labeling in NLP refers to:
- Tagging parts of speech (nouns, verbs)
- Identifying names or places (entity recognition)
- Classifying the mood of a sentence (sentiment analysis)
In this type, human annotators read through text or audio and label them. Such tagging helps tools like:
- Spell checkers
- Translation apps
- Voice assistants (like Siri or Alexa)
- Chatbots
Additionally, NLP also allows AI to summarize articles, translate sentences, or even detect sarcasm or slang. All of these are often hard for machines to understand without proper labeling by humans.
How Does AI Data Labeling Happen?
Studies show that around 78% of companies use AI in at least one part of their business. Mostly, this segment is:
- Customer service (like chatbots)
- Marketing (like content creation)
- Operations (like inventory management)
The quality of the AI models used in these segments largely depends on the accuracy of AI data labeling. Want to know how it is done? Below are some key approaches to tagging followed by most AI data labeling companies:
1. Internal Manual Labeling
In this method, your own team or experts label the data. They check each image, text, or file and add labels by hand using their knowledge. This method gives the best quality but takes time, money, and skilled people.
2. External Manual Labeling (Crowdsourcing)
Here, you give the AI data labeling job to outside workers or freelancers. You delegate the work and give clear instructions. Such outsourcing saves costs. However, it causes issues like:
- Low-quality work
- Privacy issues
- Lack of expert knowledge
3. Semi-Supervised Labeling
This AI data labeling method starts with a small amount of labeled data. You use that to train simple AI models. Then, these models label the rest of the data. In this method, if multiple models agree on a label, it’s accepted!
This method saves time and cost but only works if the data is well-structured. It’s not ideal for complex or messy data.
4. Automated Data Labeling (Model Distillation)
In this approach, you use a powerful AI model (like ChatGPT) to label your data. This model becomes the “teacher” and trains a smaller “student” model. It is fast and needs little human effort. However, sometimes the results are good enough for serious business use.
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AI data labeling is the foundation of every smart AI model. Accurate tagging allows machines to understand, predict, and perform better. It comes in various types, such as:
- Image processing
- NLP
- Video annotation
- Data digitisation, and more.
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FAQs on AI Data Labeling
1. Why is high-quality AI data labeling important for AI models’ accuracy?
High-quality labels allow AI models to understand the real-world context behind data. On the other hand, poor labeling leads to:
- Misinterpretation
- Wrong predictions
- Unreliable outputs
Thus, accurate AI data labeling is necessary to ensure that AI systems learn correctly and deliver consistently.
2. Why is manual AI data labeling so expensive and time-consuming?
Manual AI data labeling needs skilled human input (particularly for complex data like medical images or legal text). Additionally, it is slow as it requires extensive quality checks. This increases both costs and the labour hours.
3. Can I fully automate data labeling with AI?
Full automation is possible but risky. Instead, you can use AI to assist in the tagging process through:
- Pre-annotations
- Model predictions
Please note human oversight is critical to maintain accuracy!
4. How do I scale my AI data labeling without compromising quality?
Ideally, you should use a mix of these labeling strategies:
- Manual
- Semi-supervised
- Automated
Let’s see how you can do this :
- Firstly, manually label a small, high-quality set of data. This acts as your “foundation” and trains the initial AI model.
- Train a strong AI model (like ChatGPT) on this initial dataset.
- Now, let it label a larger volume of new data automatically.
- Finally, have human experts go through the AI-labeled data.
- They will correct errors and maintain accuracy.
